Uncertainty Quantification in Viscous Hypersonic Flows using Gradient Information and Surrogate Modeling

2011 ◽  
Author(s):  
Brian Lockwood ◽  
Markus Rumpfkeil ◽  
Wataru Yamazaki ◽  
Dimitri Mavriplis
Keyword(s):  
Uncertainty Quantification ◽  
Surrogate Modeling ◽  
Hypersonic Flows ◽  
Gradient Information

Polynomial-Chaos–Kriging with Gradient Information for Surrogate Modeling in Aerodynamic Design

AIAA Journal ◽  
2021 ◽  
pp. 1-18
Author(s):  
Lavi R. Zuhal ◽  
Kemas Zakaria ◽  
Pramudita Satria Palar ◽  
Koji Shimoyama ◽  
Rhea Patricia Liem
Keyword(s):  
Polynomial Chaos ◽  
Surrogate Modeling ◽  
Aerodynamic Design ◽  
Gradient Information

Uncertainty Quantification in Metallic Additive Manufacturing Through Data-Driven Modelling Based on Multi-Scale Multi-Physics Models and Limited Experiment Data

2020 ◽  
Author(s):  
Zhuo Wang ◽  
Chen Jiang ◽  
Mark F. Horstemeyer ◽  
Zhen Hu ◽  
Lei Chen
Keyword(s):  
Temperature Field ◽  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
Surrogate Modeling ◽  
Data Driven ◽  
Modeling Framework ◽  
High Quality ◽  
Experimental Calibration ◽  
Multi Scale ◽  
Metallic Additive

Abstract One of significant challenges in the metallic additive manufacturing (AM) is the presence of many sources of uncertainty that leads to variability in microstructure and properties of AM parts. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production. A trial-and-error approach usually needs to be employed to attain a product with high quality. To achieve a comprehensive uncertainty quantification (UQ) study of AM processes, we present a physics-informed data-driven modeling framework, in which multi-level data-driven surrogate models are constructed based on extensive computational data obtained by multi-scale multi-physical AM models. It starts with computationally inexpensive metamodels, followed by experimental calibration of as-built metamodels and then efficient UQ analysis of AM process. For illustration purpose, this study specifically uses the thermal level of AM process as an example, by choosing the temperature field and melt pool as quantity of interest. We have clearly showed the surrogate modeling in the presence of high-dimensional response (e.g. temperature field) during AM process, and illustrated the parameter calibration and model correction of an as-built surrogate model for reliable uncertainty quantification. The experimental calibration especially takes advantage of the high-quality AM benchmark data from National Institute of Standards and Technology (NIST). This study demonstrates the potential of the proposed data-driven UQ framework for efficiently investigating uncertainty propagation from process parameters to material microstructures, and then to macro-level mechanical properties through a combination of advanced AM multi-physics simulations, data-driven surrogate modeling and experimental calibration.

scholarly journals Multi-objective parameter optimization of common land model using adaptive surrogate modeling

2015 ◽  
Vol 19 (5) ◽  
pp. 2409-2425 ◽  
Author(s):  
W. Gong ◽  
Q. Duan ◽  
J. Li ◽  
C. Wang ◽  
Z. Di ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Parameter Optimization ◽  
Land Surface ◽  
Dynamic Models ◽  
Weighting Function ◽  
Regional Scale ◽  
Surrogate Modeling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Common Land Model

Abstract. Parameter specification usually has significant influence on the performance of land surface models (LSMs). However, estimating the parameters properly is a challenging task due to the following reasons: (1) LSMs usually have too many adjustable parameters (20 to 100 or even more), leading to the curse of dimensionality in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) Regional LSMs are expensive to run, while conventional multi-objective optimization methods need a large number of model runs (typically ~105–106). It makes parameter optimization computationally prohibitive. An uncertainty quantification framework was developed to meet the aforementioned challenges, which include the following steps: (1) using parameter screening to reduce the number of adjustable parameters, (2) using surrogate models to emulate the responses of dynamic models to the variation of adjustable parameters, (3) using an adaptive strategy to improve the efficiency of surrogate modeling-based optimization; (4) using a weighting function to transfer multi-objective optimization to single-objective optimization. In this study, we demonstrate the uncertainty quantification framework on a single column application of a LSM – the Common Land Model (CoLM), and evaluate the effectiveness and efficiency of the proposed framework. The result indicate that this framework can efficiently achieve optimal parameters in a more effective way. Moreover, this result implies the possibility of calibrating other large complex dynamic models, such as regional-scale LSMs, atmospheric models and climate models.

scholarly journals Deep Active Subspaces: A Scalable Method for High-Dimensional Uncertainty Propagation

2019 ◽  
Author(s):  
Rohit Tripathy ◽  
Ilias Bilionis
Keyword(s):  
Dimensionality Reduction ◽  
Stochastic Systems ◽  
Uncertainty Propagation ◽  
Gaussian Process Regression ◽  
Surrogate Modeling ◽  
Forward Model ◽  
High Dimensional ◽  
Great Promise ◽  
Gradient Information ◽  
Active Subspaces

Abstract A problem of considerable importance within the field of uncertainty quantification (UQ) is the development of efficient methods for the construction of accurate surrogate models. Such efforts are particularly important to applications constrained by high-dimensional uncertain parameter spaces. The difficulty of accurate surrogate modeling in such systems, is further compounded by data scarcity brought about by the large cost of forward model evaluations. Traditional response surface techniques, such as Gaussian process regression (or Kriging) and polynomial chaos are difficult to scale to high dimensions. To make surrogate modeling tractable in expensive high-dimensional systems, one must resort to dimensionality reduction of the stochastic parameter space. A recent dimensionality reduction technique that has shown great promise is the method of ‘active subspaces’. The classical formulation of active subspaces, unfortunately, requires gradient information from the forward model — often impossible to obtain. In this work, we present a simple, scalable method for recovering active subspaces in high-dimensional stochastic systems, without gradient-information that relies on a reparameterization of the orthogonal active subspace projection matrix, and couple this formulation with deep neural networks. We demonstrate our approach on challenging synthetic datasets and show favorable predictive comparison to classical active subspaces.

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